| from transformers import PretrainedConfig | |
| class GPTBertConfig(PretrainedConfig): | |
| model_type = 'gpt_bert' | |
| def __init__(self, **kwargs): | |
| self.attention_probs_dropout_prob = kwargs.pop('attention_probs_dropout_prob', 0.1) | |
| self.hidden_dropout_prob = kwargs.pop('hidden_dropout_prob', 0.1) | |
| self.hidden_size = kwargs.pop('hidden_size', 768) | |
| self.intermediate_size = kwargs.pop('intermediate_size', 2560) | |
| self.max_position_embeddings = kwargs.pop('max_position_embeddings', 512) | |
| self.position_bucket_size = kwargs.pop('position_bucket_size', 32) | |
| self.num_attention_heads = kwargs.pop('num_attention_heads', 12) | |
| self.num_hidden_layers = kwargs.pop('num_hidden_layers', 12) | |
| self.vocab_size = kwargs.pop('vocab_size', 16384) | |
| self.layer_norm_eps = kwargs.pop('layer_norm_eps', 1e-5) | |
| self.force_causal_mask = kwargs.pop('force_causal_mask', True) | |
| self.classifier_dropout = kwargs.pop('classifier_dropout', 0.1) | |
| self.classifier_layer_norm_eps = kwargs.pop('classifier_layer_norm_eps', 1e-05) | |
| self.num_labels = kwargs.pop('num_labels', 2) | |
| self.problem_type = kwargs.pop('problem_type', None) | |
| self.auto_map = { | |
| 'AutoConfig': 'configuration_gpt_bert.GPTBertConfig', | |
| 'AutoModel': 'modeling_gpt_bert.GPTBertForMaskedLM', | |
| 'AutoModelForCausalLM': 'modeling_gpt_bert.GPTBertForCausalLM', | |
| 'AutoModelForMaskedLM': 'modeling_gpt_bert.GPTBertForMaskedLM', | |
| 'AutoModelForSequenceClassification': 'modeling_gpt_bert.GPTBertForSequenceClassification', | |
| } | |
| super().__init__(**kwargs) | |